Comprehensive Colorectal Cancer Risk Prediction to Inform Personalized Screening

全面的结直肠癌风险预测为个性化筛查提供信息

基本信息

批准号：
9237818
负责人：
DOUGLAS Allen CORLEY
金额：
$ 73.84万
依托单位：
FRED HUTCHINSON CANCER RESEARCH CENTER
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-01-03 至 2020-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9237818
关键词：
Adherence Advisory Committees Age Alcohols California Cancer Etiology Cancer Intervention and Surveillance Modeling Network Case-Control Studies Cessation of life Characteristics Clinical Colon Colorectal Adenoma Colorectal Cancer Colorectal Neoplasms Communities Cost Effectiveness Analysis Coupled Data Decision Modeling Diet Early Diagnosis Early identification Ensure Environment Environmental Risk Factor Epidemiology Ethnic Origin Evaluation Excision Family Family history of Genes Genetic Genetic Research Genetic Risk Genetic screening method Genotype Guidelines Health Individual Intervention Knowledge Life Expectancy Life Style Machine Learning Malignant Neoplasms Medical Records Methods Minority Modeling National Cancer Institute Obesity Outcome Participant Patient Care Persons Pharmaceutical Preparations Physical activity Physicians Policy Developments Population Predictive Factor Preventive Preventive service Primary Prevention Prospective cohort Public Health Recommendation Recording of previous events Research Resources Risk Risk Factors Sampling Science Screening for cancer Secondary Prevention Signal Transduction Smoking Technology Testing Training Translations United States Variant Women&apos s Health Work adenoma base cancer prevention cancer risk clinical practice clinical translation cohort colorectal cancer prevention colorectal cancer screening community based participatory research comparative cost cost effective cost effectiveness demographics disorder risk epidemiologic data epidemiology study ethnic minority population functional genomics gene environment interaction genetic variant genome sequencing genome wide association study genome-wide genomic data health care delivery member non-genetic novel personalized decision personalized medicine personalized screening population based precision medicine predictive modeling programs racial and ethnic rare variant routine care screening socioeconomics tool whole genome

项目摘要

PROJECT SUMMARY The guidelines for initiation of colorectal cancer (CRC) screening are currently based on two risk factors: attained age and family history of CRC. Using the principles of precision medicine, we will individually tailor CRC screening recommendations based on the enormous knowledge we now have on genetic and non- genetic factors that predict risk for this disease. This strategy will reduce under and over-utilization of CRC screening, because individual risks vary substantially in the population, and over 80% of all CRC cases occur in those without a positive family history. In Aim 1 we will develop predictive models for CRC, based on genetic (~30M common and rare genotyped or imputed genetic variants) and clinico-epidemiologic variables (over 70 harmonized characteristics), derived from over 40,000 colorectal tumor cases and 46,000 controls. We will identify key predictors, and derive efficient personalized risk-prediction models for early detection of more treatable CRCs as well for CRC prevention, through the identification of advanced colorectal adenomas. In Aim 2 we will calibrate and validate these models in two prospective cohorts of >120,000 participants, including 40,000 minority members, diversity representing racially/ethnically and socioeconomically. These two cohorts (Research Program on Genes, Environment and Health and the Women's Health Initiative Minority cohort) contain genome-wide genotype array and comprehensive risk factor data coupled to data on screening and outcome data, allowing us to test the model's ability to predict risk for CRC and advanced colorectal adenoma across a broadly defined community-based population, and to personalize decision on starting age of screening based on individually specific genetic and environmental risk factors. In Aim 3 we will estimate the population benefit of our risk-stratified screening strategy, based on our risk prediction methods, compared with the current screening recommendations. This comparison will employ the well-tested decision model currently used to inform the United States Preventive Services Task Force CRC screening guidelines. Accomplishing these three aims, our research has the potential to accelerate the translation of a large amount of genetic and epidemiologic research to patient care by predicting advanced adenoma risk, for cancer prevention, and predicting cancer risk, for early cancer detection. Genetic testing is becoming part of routine care and genetic data will increasingly become part of an individual's medical record. Using genetic and non-genetic risk-factor information in clinical and preventive settings is a critical step towards developing precision medicine. Our models will provide recommendations for individually tailored CRC screening and interventions and, because they are personalized, may also increase adherence, maximize the appropriate use of invasive technologies, and guide important next steps towards public health policy development and clinical translation.

项目概要目前，启动结直肠癌 (CRC) 筛查的指南基于两个风险因素：达到的年龄和 CRC 家族史。利用精准医学的原理，我们会为您量身定制 CRC 筛查建议基于我们现有的遗传和非遗传性知识预测这种疾病风险的遗传因素。该策略将减少 CRC 的利用不足和过度利用筛查，因为人群中的个体风险差异很大，并且超过 80% 的 CRC 病例发生对于那些没有阳性家族史的人。在目标 1 中，我们将开发 CRC 预测模型，基于遗传（约 30M 常见和罕见基因分型或推定遗传变异）和临床流行病学变量（超过 70 个协调特征），源自 40,000 多个结直肠肿瘤病例和 46,000 个对照。我们将确定关键预测因素，并得出有效的个性化风险预测模型，以便及早发现通过识别晚期结直肠腺瘤，可以治疗更多的结直肠癌以及预防结直肠癌。在目标 2 中，我们将在两个超过 120,000 名参与者的前瞻性队列中校准和验证这些模型，包括 40,000 名少数族裔成员，体现了种族/民族和社会经济的多样性。这两个队列（基因、环境与健康研究计划和妇女健康倡议少数群体队列）包含全基因组基因型阵列和综合风险因素数据以及筛查数据和结果数据，使我们能够测试模型预测 CRC 和晚期结直肠癌风险的能力腺瘤跨越广泛定义的社区人群，并个性化决定起始年龄根据个体特定的遗传和环境风险因素进行筛查。在目标 3 中，我们将估计基于我们的风险预测方法，我们的风险分层筛查策略的人群效益进行了比较根据当前的筛查建议。这种比较将采用经过充分测试的决策模型目前用于告知美国预防服务工作组 CRC 筛查指南。实现这三个目标，我们的研究有可能加速大量成果的翻译通过预测晚期腺瘤风险来进行患者护理的遗传和流行病学研究，以预防癌症，预测癌症风险，以实现早期癌症检测。基因检测正在成为日常护理的一部分遗传数据将越来越多地成为个人医疗记录的一部分。利用遗传和非遗传临床和预防环境中的危险因素信息是提高精确度的关键一步药品。我们的模型将为个性化的 CRC 筛查和干预提供建议而且，由于它们是个性化的，还可以增加依从性，最大限度地适当使用侵入性方法技术，并指导公共卫生政策制定和临床转化的重要后续步骤。